Abstract:

Forest management is typically associated with a high degree of uncertainty,
since it relies on predictions of natural growth processes over long periods of time. A
number of methods exist for mitigating the risk associated with this uncertainty, but
few have the ability to explicitly minimize risk. This study will present a case study on
dealing with uncertainty and risk in an applied setting. The selected study area was
the Tillamook State Forest, located in northwest Oregon. The primary objectives
were to quantify the uncertainty and assess its impact on forest management. An
additional objective was to assess the application of non-linear probabilistic programming on a large forest management problem. Uncertainty was quantified
through regression models that predicted actual outcomes from planned outcomes,
as well as the error associated with predictions of actual outcomes. The effects of
uncertainty on forest management were assessed through two chance-constrained
programming formulations. One maximized the harvest volume under a given level
of risk, and the other minimized the maximum level of risk associated with a given
forest management plan. Both were subject to sustainable inventory and forest
structure constraints. The results showed that these models could substantially
increase the probability of achieving a given forest management outcome, at the cost
of only a minimal deviation (4 to 6%) from the risk neutral position. These results
were however in contrast to an analysis of risk preferences, which showed significant
differences in the outcomes associated with various levels of risk. This indicated that
uncertainty could not be considered without the decision maker's attitude towards
risk. In addition, post-optimality analysis of the model results showed that correlated
yield coefficients had an insignificant impact, and that the assumption of zero
covariance was justified for this study. Finally, it was also demonstrated that chance-
constrained programming can be applied to large scale forest management
problems, but that the solvability of these problems were determined by the
formulation type.